Development of A Novel Decision Aid for Informed Decision-Making of Intraocular Lens Types in Patients Undergoing Cataract Surgery
Bibliographic record
Abstract
Abstract Objective Surgery is the main treatment of visual loss related to cataracts. There are multiple intraocular lens (IOL) options with certain advantages. Patient education on IOL types is necessary to achieve a successful shared decision making process and meet the expectations of the individual patient. Decision aids (DAs) are used for patient education and we developed a novel DA to assist patients during IOL type selection for their cataract surgery. Methods The Ottawa Personal Decision Guide and the ‘Workbook on Developing and Evaluating Patient Decision Aids’ were used in the development of this DA. General characteristics of cataracts, surgical treatment, and details including advantages and disadvantages of varying IOLs were included in the content of the DA. The DA was further evaluated by 3 physicians (Delphi assessment- International Patient Decision Aid Standards (IPDAS) Collaboration standards) and 25 patients (questionnaire of 6 questions with Five-point Likert scale). Results The DA was finalized with feedbacks from the experts. A total score of 50/54 was achieved in Delphi group assessment. Patient perception of the DA was favorable and patients also recommended its use by other patients. Conclusions This novel DA to assist IOL selection for cataract surgery was well accepted by the patients. There is a potential to improve patients’ level of knowledge and diminish decisional conflicts. This potential can also increase patients’ contribution on the shared decision making process. A further prospective randomized trial to compare with the standard patient informing process is also planned.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".